...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Structural health monitoring >Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles
【24h】

Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles

机译：基于三维图像坐标缺失区域的兴趣区检测和桥梁目视检查损伤定位，使用无人驾驶飞行器

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this study, the three-phase missing region of interest area detection and damage localization methodology based on three-dimensional image coordinates was proposed. In Phase 1, the coordinate transformation is performed by the position and attitude information of the unmanned aerial vehicles and camera, and the coordinates of the center point of each acquired image are obtained with the distance information between the camera and the target surface. For Phase 2, the size of the field of view of every acquired image is calculated using the focal length and working distance of the camera. Finally, in Phase 3, the missing part of the region of interest area can be identified and any damage detected at the individual image level can also be localized on the whole inspection region using information about the sizes of the field of view in all images calculated in the previous phase. In order to demonstrate the proposed methodology, experimental validation was performed on the actual bridge pier and deck as well as the lab-scale concrete shear wall. In the tests, the missing area detection and damage localization results were compared with image stitching and human visual inspection results, respectively. Experimental validation results have shown that the proposed methodology identifies missing areas and damage locations within reasonable accuracy of 10 cm.
机译：在这项研究中，提出了基于三维图像坐标的三相缺失区域检测和损坏定位方法。在阶段1中，通过无人驾驶飞行器和相机的位置和姿态信息来执行坐标变换，并且通过相机和目标表面之间的距离信息获得每个获取的图像的中心点的坐标。对于阶段2，使用相机的焦距和工作距离来计算每个获取的图像的视野的大小。最后，在阶段3中，可以识别感兴趣区域区域的缺失部分，并且在各个图像级别检测到的任何损坏也可以在整个检查区域中使用关于所计算的所有图像中的视野的尺寸的信息来定位在整个检查区域上在前一阶段。为了证明所提出的方法，对实际桥接码头和甲板进行实验验证以及实验室规模的混凝土剪力墙。在测试中，将缺失的区域检测和损坏定位结果分别与图像缝合和人观检测结果进行比较。实验验证结果表明，所提出的方法在10厘米的合理精度范围内识别缺失的区域和损坏位置。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号